Design and Implementation of Network Security Management System Based on K-Means Algorithm

With the rapid development of computer technology and Internet worldwide, people are using computers more and more to deal with various affairs. However, due to the late start of information construction and weak foundation, there are still many problems that need to be solved in China: such as unreasonable network structure, unsound network management system and lack of effective network security protection system, which have seriously restricted the rapid and healthy development of China’s economy and society. Therefore, how to strengthen the scientific and rational use of information network resources has become one of the important issues that need to be studied urgently. As an emerging information processing method, cluster analysis is to measure the similarity in a collection of objects and divide them into several categories according to certain criteria, so that the objects in the same category have a greater degree of similarity or similarity. It can discover a large amount of potentially useful knowledge, and thus is widely used in pattern recognition, machine learning, image segmentation and text classification. However, traditional clustering algorithms often need to specify the number of clusters or the initial centroid positions in advance, which leads to the final result may not be the optimal solution; In addition, when the size of the dataset increases, the computational effort also increases dramatically, which is difficult to meet the actual demand. To address the above problems, this paper proposes a new K-Means algorithm-based model for network security management systems, which can automatically determine the number of clusters and their optimal initial values, while using a parallel approach to execute tasks to reduce running time and improve efficiency.